Within a pavement management system, pavement condition data are used for modeling pavement performance; to trigger maintenance, rehabilitation, and reconstruction; to evaluate program effectiveness; and to satisfy many other purposes. While there are many different methodologies used for assessing pavement condition (i.e., manual, semi- and fully automated surveys), the need for quality data remains the same. Agencies take a number of steps to ensure and verify data quality, including calibration of the data collection equipment or the inspection teams, incorporating quality control sections that are re-inspected to assess repeatability, verifying reasonableness and completeness of the pavement condition survey, and conducting audits of the pavement distress data. As part of a Federal Highway Administration project, a Practical Guide for Quality Management of Network-Level Pavement Condition Data was developed based on agency procedures, practical examples, and case studies. This paper summarizes the components of a data quality management plan for pavement condition data collection and when applicable, provides examples of agency practices. The primary activities involved in developing a data quality management plan include identifying what data quality standards will be used, identifying what activities need to occur to achieve those standards, measuring the data, and reporting the results. Specifically related to pavement condition data collection, the key components of a data quality management plan include establishing data collection/rating protocols, defining data quality standards, determining personnel responsibilities, providing personnel training programs, establishing equipment calibration and method acceptance, conducting data inspection, applying corrective action, and reporting the results of the quality management process.